What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His primary areas of study are Artificial intelligence, Artificial neural network, Recurrent neural network, Mathematical optimization and Pattern recognition.
His Artificial intelligence research incorporates themes from Machine learning, Dynamic programming and Computer vision.
His study in the field of Backpropagation and Backpropagation through time is also linked to topics like Elementary function.
His Recurrent neural network research is multidisciplinary, incorporating perspectives in Speech recognition, Exponential stability, Extended Kalman filter and Electroencephalography.
Danil V. Prokhorov works mostly in the field of Mathematical optimization, limiting it down to topics relating to Reinforcement learning and, in certain cases, Reinforcement, Implementation and Optimal control.
In the subject of general Pattern recognition, his work in Feature extraction, Wavelet transform and Convolutional neural network is often linked to Proximal Gradient Methods, thereby combining diverse domains of study.
His most cited work include:
- Adaptive critic designs (1034 citations)
- MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking (501 citations)
- Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks (414 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Artificial neural network, Computer vision, Recurrent neural network and Machine learning.
His work on Pattern recognition expands to the thematically related Artificial intelligence.
His work carried out in the field of Artificial neural network brings together such families of science as Kalman filter and Control system.
His is doing research in Video tracking, Cognitive neuroscience of visual object recognition, Tracking and Contextual image classification, both of which are found in Computer vision.
His work investigates the relationship between Recurrent neural network and topics such as Control theory that intersect with problems in Control.
His study brings together the fields of Mathematical optimization and Adaptive control.
He most often published in these fields:
- Artificial intelligence (46.00%)
- Artificial neural network (25.00%)
- Computer vision (18.00%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (46.00%)
- Acoustics (5.00%)
- Actuator (4.67%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Acoustics, Actuator, Computer vision and Artificial neural network.
Danil V. Prokhorov has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His study explores the link between Machine learning and topics such as Inference that cross with problems in Pixel.
His Acoustics research incorporates elements of Medial surface and Aerodynamics.
His study in Artificial neural network is interdisciplinary in nature, drawing from both Control system and Mathematical optimization.
His studies deal with areas such as Classifier, Recurrent neural network, Image segmentation and Brake as well as Convolutional neural network.
Between 2015 and 2021, his most popular works were:
- Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene (206 citations)
- Approximation with random bases (78 citations)
- Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning (75 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Electrical engineering
The scientist’s investigation covers issues in Artificial intelligence, Deep learning, Convolutional neural network, Simulation and Pattern recognition.
His Artificial intelligence study combines topics in areas such as Machine learning, Vehicle dynamics and Computer vision.
The various areas that Danil V. Prokhorov examines in his Computer vision study include Control and Headlamp.
In his study, Sensitivity is inextricably linked to Artificial neural network, which falls within the broad field of Deep learning.
Danil V. Prokhorov regularly links together related areas like Recurrent neural network in his Convolutional neural network studies.
His work deals with themes such as Boosting, Task analysis and Feature, which intersect with Pattern recognition.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
